Contents
This presentation will be divided into two parts. As enterprises integrate LLMs into localization, accuracy and control become the main challenges. This session explores a framework for reducing human-in-the-loop dependency without sacrificing linguistic integrity. Using SAE J2450 as a foundational standard, we validate content and TM at scale, deploying custom AI prompts and AI-enhanced APE to lock high-quality segments, freeing reviewers to focus on genuine errors. AutoLQA then continuously verifies quality at volume, closing the loop. Attendees leave with a path from manual, sampling-based review to high-velocity automated validation.
Speakers
Biography
Biography